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使用深度学习进行基于声音的超体症状评估

Erandhi M Liyanage1, Kun-Chan Lan2, Quang Ha1

  • 1School of Electrical and Data Engineering, University of Technology Sydney, 15 Broadway, Ultimo, Sydney, NSW 2007, Australia.

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概括

这项研究引入了一种基于语音的新型AI模型,用于评估超体症状 (EPS) 的严重程度. 该模型使用语音记录的声学特征准确预测EPS,提供了一个新的客观评估工具.

关键词:
这是一个很大的问题.帕金森症染色体深度学习皮拉米德外症状基本频率光谱对比度语音诊断

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科学领域:

  • 神经学
  • 人工智能
  • 语言科学

背景情况:

  • 外皮拉米德症状 (EPS) 是影响手写和言语的运动障碍,通常与帕金森症有关.
  • 对EPS严重性的客观评估对于患者的治疗和治疗疗效至关重要.
  • 目前的EPS分级方法依赖于经过培训的专业人员管理的临床尺度.

研究的目的:

  • 开发和验证一个客观的,以语音为基础的工具,用于评估超体症状的严重程度.
  • 研究语音中的特定声学特征与EPS的严重程度之间的相关性.
  • 通过深度神经网络利用转移学习来自动地从语音数据中进行EPS分类.

主要方法:

  • 收集了94名不同程度的EPS患者和30名对照者的语音数据,包括母音和辅音的录音.
  • 使用药物诱导的外皮拉米德副作用尺度和格拉斯哥抗精神病副作用尺度进行了EPS严重性的临床评估.
  • 采用DenseNet架构来提取和分类语音数据,使用MFCC,色谱和光谱对比等声学特征.

主要成果:

  • 发现了Mel频率塞普斯特尔系数 (MFCC),色谱和光谱对比度的显著变化,并增加了EPS的严重程度.
  • 一个基于DenseNet的模型实现了81.9%的准确性和82.0%的准确性,用于从语音数据中分类EPS严重程度.
  • 这是第一个展示客观EPS严重性评估的基于语音模型的研究.

结论:

  • 语音分析,特别是特定的声学特征,可以作为超层次症状严重性的客观生物标志物.
  • 开发的DenseNet模型为早期发现和监测EPS提供了一个有希望的,非侵入性的工具.
  • 这种基于语音的方法有可能提高与EPS相关的临床评估和管理.